Security group implementation method based on Paas cloud platform and method for optimizing network topology management

Abstract

The invention provides a security group implementation method based on a Paas cloud platform and a method for optimizing network topological structure management, and the method comprises the steps: collecting and processing network traffic data, and forming a data set containing normal traffic and malicious traffic; analyzing and predicting a network security threat in real time through an LSTM network based on the data set; normal traffic and malicious traffic are detected in combination with an SVM and a random forest algorithm; and network topology management is optimized by using a graph algorithm and a distributed computing method. According to the invention, the deep learning algorithm is adopted to carry out real-time threat analysis and prediction, so that the detection and defense capabilities for zero-day vulnerabilities and advanced threats are improved; the SVM and the random forest algorithm are adopted to classify and identify normal and malicious traffic, so that the accuracy and the response speed of the security group are improved; and network management and resource allocation are optimized by adopting a graph algorithm and a distributed computing method, so that the controllability and security of network security are improved.

Description

Technical Field

[0001] The present invention belongs to the field of cloud computing and network security, and specifically relates to a security group implementation method based on a Paas cloud platform and a method for optimizing network topology management. Background Art

[0002] Security group technology is an important network security technology, mainly used in cloud computing and network security. In traditional network security architecture, access control based on ports and IP addresses is usually adopted, which makes it difficult to finely control network traffic and is vulnerable to various network attacks and threats. In order to solve this problem, security group technology came into being.

[0003] Security group technology is a rule-based access control technology that can fine-tune the control and management of network traffic. A security group is a virtual firewall that can limit the inbound and outbound traffic of virtual machines or container instances by configuring security group rules. In cloud computing, security groups are an important means to ensure system security and stability, and can effectively prevent unauthorized access, malicious attacks, data leaks and other security issues.

[0004] The emergence of security group technology not only improves the controllability and security of network security, but also provides strong support for the development and popularization of cloud computing. In cloud computing, users can flexibly configure and manage security group rules according to their own needs to achieve security isolation and access control of different applications and services. At the same time, security group technology also supports automated and dynamic management, and can automatically adjust and update security group rules as network topology changes and application service migrations, improving the real-time and responsiveness of network security.

[0005] Security group technology is an indispensable technology in the field of cloud computing and network security. It can improve the controllability and security of network security and provide important support for the development and popularization of cloud computing. However, the defects and shortcomings of security group management technology are mainly reflected in the difficulty in dealing with complex and changing network security threats, especially the lack of effective detection and defense methods for zero-day vulnerabilities and advanced threats; there may be problems of misjudgment and omission, resulting in normal traffic being mistaken for malicious traffic or malicious traffic being missed; there are difficulties in managing large-scale networks and complex network topologies. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a security group implementation method based on a Paas cloud platform and a method for optimizing network topology management. The security and stability of the system are guaranteed by performing refined access control on network traffic. For example, in a cloud server, the inbound and outbound traffic of the server can be restricted by security group rules to avoid unauthorized access and malicious attacks; in an enterprise internal network, the access rights of different users or departments can be divided and managed by security group technology to prevent sensitive data from being illegally accessed and leaked.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a security group implementation method based on a Paas cloud platform, characterized in that it includes:

[0009] Collect and process network traffic data to form a data set containing normal and malicious traffic;

[0010] Based on the data set, the LSTM network is used to analyze and predict network security threats in real time;

[0011] Combine SVM and random forest algorithms to detect normal and malicious traffic;

[0012] Optimize network topology management using graph algorithms and distributed computing methods.

[0013] Optionally, in the data set, the proportion of normal traffic is 80%, and the proportion of malicious traffic is 20%; the data in the data set is preprocessed and feature extracted, including data cleaning, data normalization, data dimensionality reduction and feature selection.

[0014] Optionally, the LSTM network adopts a network structure including two LSTM layers and one fully connected layer, selects cross entropy as the loss function, adopts Adam optimization algorithm for network training, and divides the data set into a training set and a test set in a ratio of 8:2.

[0015] Optionally, the combination of SVM and random forest algorithm to detect and classify normal traffic and malicious traffic is specifically as follows:

[0016] SVM classifier and random forest classifier are used to detect normal traffic and malicious traffic at the same time; when the SVM classifier and random forest classifier are trained on the training data, the performance of each classifier is evaluated by calculating the accuracy, precision and recall rate for the detection results of the two classifiers, and the SVM classifier and random forest classifier are optimized respectively by adjusting the kernel function type of the SVM and the number and depth of the trees in the random forest.

[0017] Optionally, the optimizing network topology management by using graph algorithms and distributed computing methods specifically includes:

[0018] Collect node and edge data in the network through network monitoring tools or log systems. The virtual machines and network connections are considered as a directed graph, where each node represents a virtual machine and each edge represents the connection relationship between virtual machines.

[0019] Build a PySpark environment on the Paas cloud platform, install the dependent library GraphFrames, and configure Spark session and SparkContext;

[0020] Use the collected node and edge data to construct a GraphFrame graph object;

[0021] Call the Page Rank method to calculate the Page Rank value of each node in the GraphFrame graph object;

[0022] Sort the nodes according to Page Rank values ​​and filter out key nodes;

[0023] Allocate resources and set protection measures based on the selected key nodes.

[0024] Optionally, when constructing the GraphFrame graph object, an initial weight is set for each node according to the characteristics and performance indicators of the virtual machine.

[0025] Optionally, in the process of calculating the Page Rank value of each node, the safety score and reliability score of the node are combined with the damping factor to correct the Page Rank value of the node.

[0026] Optionally, the calculation formula of the Page Rank value of each node is as follows:

[0027] PR(i)=(1-d) / N+d*∑((PR(j) / L(j))*(1-α*P(i)-β*Q(i)));

[0028] Where PR(i) and PR(j) represent the Page Rank values ​​of node i and node j respectively; d represents the damping factor; N represents the total number of nodes in the directed graph; L(j) is the out-degree of node j, which represents the number of edges connected to node j; P(i) and Q(i) represent the safety score and reliability score of node i respectively, α represents the weight of the safety factor, and D represents the weight of the reliability factor.

[0029] Optionally, the calculation process of the safety score and reliability score of the node i is as follows:

[0030] Assume that the path from node i to node k is i->n1->n2->…->nk, where ni represents the intermediate node on the path; the number of safe paths s(i, k) and the number of reliable paths r(i, k) from node i to node k are calculated by the following formula:

[0031] s(i,k)=Π(s(ni-1,ni)*c1(ni));

[0032] r(i,k)=Π(r(ni-1,ni)*c2(ni));

[0033] In the formula, s(ni-1, ni) represents the number of safe paths from node ni-1 to node ni, c1(ni) represents the safety weight from node ni to nk; r(ni-1, ni) represents the number of reliable paths from node ni-1 to node ni; c2(ni) represents the reliability weight from node ni to nk

[0034] Calculate the safety score of a node by counting the number of safe paths to the node:

[0035] P(i)=∑(s(i,k)) / ∑(L(i));

[0036] Q(i)=∑(r(i,k)) / ∑(L(i));

[0037] Where L(i1) is the out-degree of node i, which indicates the number of edges connected to node i.

[0038] In a second aspect, the present invention provides a method for optimizing network topology management based on a Paas cloud platform security group, characterized in that it includes:

[0039] Collect node and edge data in the network through network monitoring tools or log systems. The virtual machines and network connections are considered as a directed graph, where each node represents a virtual machine and each edge represents the connection relationship between virtual machines.

[0040] Build a PySpark environment on the Paas cloud platform, install the dependent library GraphFrames, and configure Spark session and SparkContext;

[0041] Use the collected node and edge data to construct a GraphFrame graph object;

[0042] Call the Page Rank method to calculate the Page Rank value of each node in the GraphFrame graph object;

[0043] Sort the nodes according to Page Rank values ​​and filter out key nodes;

[0044] Allocate resources and set protection measures based on the selected key nodes.

[0045] The beneficial effects of the present invention are:

[0046] 1. The present invention uses machine learning and artificial intelligence technologies, such as deep learning algorithms, to perform real-time threat analysis and prediction, solving the problem that traditional access control methods based on ports and IP addresses are difficult to cope with complex and changeable network security threats, and improving the detection and defense capabilities of zero-day vulnerabilities and advanced threats.

[0047] 2. For the classification and identification of normal and malicious traffic, the present invention adopts support vector machine (SVM) and random forest algorithm to classify and identify normal and malicious traffic, which solves the problem that traditional access control methods based on ports and IP addresses are difficult to finely control network traffic, and improves the accuracy and response speed of security groups.

[0048] 3. For large-scale and complex network topologies, the present invention adopts graph algorithms and distributed computing methods, especially the PageRank algorithm, to optimize network management and resource allocation, solving the problem that traditional security group technology is difficult to cope with the management of large-scale and complex network topologies. It can efficiently manage and control large-scale networks and optimize the allocation and utilization efficiency of network resources.

[0049] In summary, the present invention makes up for the shortcomings of traditional security group technology and improves the controllability and security of network security by introducing machine learning and artificial intelligence technologies, as well as graph algorithms and distributed computing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a security group implementation method based on Paas cloud platform of the present invention.

[0051] Figure 2 It is a flow chart of a method for optimizing network topology management based on a Paas cloud platform security group of the present invention. DETAILED DESCRIPTION

[0052] The present invention will now be described in further detail with reference to the accompanying drawings.

[0053] like Figure 1 As shown, the present invention proposes a security group implementation method based on Paas cloud platform, which mainly includes: real-time prediction of network security, SVM detection of malicious traffic, complex network management, Page Rank algorithm improvement, and security group implementation.

[0054] 1. Real-time prediction of network security

[0055] LSTM is used to analyze and predict network security threats in real time, thereby improving the detection and defense capabilities of zero-day vulnerabilities and advanced threats. Real-time prediction of network security requires the design of a network structure consisting of two LSTM layers and one fully connected layer, where each LSTM layer contains 64 neurons. The network traffic data is divided into a training set and a test set for network training and testing, respectively. The Adam optimization algorithm is used for network training, with a learning rate of 0.001, a batch size of 32, and 10 training rounds. In each round of training, the data set is randomly divided into several batches, each batch contains 32 samples, the network is trained based on the input of each batch, and the network performance is evaluated based on indicators such as the accuracy and recall rate of the test set.

[0056] A data set containing normal traffic and malicious traffic is used as a sample, where the proportion of normal traffic is 80% and the proportion of malicious traffic is 20%. The data set is divided into a training set and a test set in a ratio of 8:2, where the training set contains 80% of the data and the test set contains 20% of the data.

[0057] Preprocess and extract features from the raw data to convert it into a form that can be processed by the LSTM network. Common preprocessing and feature extraction methods include data cleaning, data normalization, data dimension reduction, and feature selection. For example, the raw data can be arranged in time series, and the network traffic data at each time step can be converted into a vector that contains various features and attribute information, such as source IP address, destination IP address, protocol type, port number, etc.

[0058] Design the structure and parameters of the LSTM network. In some embodiments, a network structure comprising two LSTM layers and one fully connected layer is used, wherein each LSTM layer comprises 64 neurons. It is also necessary to select a suitable loss function and evaluation index. In some embodiments, cross entropy is selected as the loss function, and accuracy and recall are used as evaluation indexes.

[0059] Conduct network training and testing. In network training, the Adam optimization algorithm is used for network training, and the learning rate is set to 0.001, the batch size is 32, and the number of training rounds is 10.

[0060] After processing the data, the accuracy rate was 95%, the recall rate was 94%, and the F1 value was 0.95. This shows that the designed LSTM network structure and parameters are relatively suitable, which can effectively predict and identify network security threats and realize the ability to predict network security.

[0061] 2. SVM detects malicious traffic

[0062] Combined with support vector machine (SVM) and random forest algorithm, normal and malicious traffic can be classified and identified, reducing the risk of misjudgment and missed judgment.

[0063] A data set containing normal traffic and malicious traffic is used as a sample, where the proportion of normal traffic is 80% and the proportion of malicious traffic is 20%. The data set is divided into a training set and a test set in a ratio of 8:2, where the training set contains 80% of the data and the test set contains 20% of the data.

[0064] Two common classification algorithms, Support Vector Machine (SVM) and Random Forest, were used to evaluate their performance by training and testing on the dataset. The following describes the entire process and the logic behind it in detail.

[0065] First, assume that there is a dataset containing feature data and label data. Feature data (X) is the input variable for classification, and label data (y) is the corresponding output category, which is assumed to be normal traffic and malicious traffic. Next, the train_test_split function is used to divide the dataset into training set and test set. The training set is used to train the model, and the test set is used to evaluate the performance of the model. In this section, the dataset is divided into 80% training data and 20% test data.

[0066] Next, two classifiers are implemented: one is a support vector machine (SVM) classifier and the other is a random forest classifier. Support vector machine is a supervised learning model commonly used for classification problems. It distinguishes samples of different categories by finding an optimal hyperplane in the feature space. In, the support vector machine uses a linear kernel function, which means that it assumes that the data can be separated by a linear hyperplane. Then, the fit method of the SVM classifier is called to train the model on the training data and use the trained model to predict the test data.

[0067] The random forest classifier is an ensemble learning method based on decision trees. It builds multiple decision trees and makes independent predictions on each tree, then votes or averages the predictions of all trees to finally get the classification result. The advantage of random forest is that it can handle high-dimensional data and can reduce the risk of overfitting. In some embodiments, the random forest classifier sets 100 trees and trains on the training data, and then also predicts on the test data.

[0068] After obtaining the prediction results of the two classifiers, the performance of the classifiers is evaluated by calculating the accuracy, precision, and recall. Accuracy refers to the ratio of the number of samples correctly classified by the classifier to the total number of samples, and is an indicator to measure the overall performance of the classifier. Precision refers to the ratio of samples predicted as positive by the classifier that are actually positive, and is mainly used to measure the accuracy of the classifier. Recall refers to the ratio of samples predicted correctly by the classifier among all samples that are actually positive, and is used to measure the sensitivity of the classifier.

[0069] The innovation here is to introduce two different classification algorithms for identifying normal traffic and malicious traffic. By using the SVM and random forest algorithms, the risk of misjudgment and missed judgment can be reduced to a certain extent, thereby improving the overall performance of the classifier. Compared with a single classification algorithm, using multiple algorithms for integrated classification can take advantage of the advantages of each algorithm, thereby improving the stability and robustness of the classifier.

[0070] In addition, this section provides a basic framework that can be further adjusted and improved according to actual needs. For example, the kernel function type of the support vector machine can be adjusted, or the number and depth of trees in the random forest can be adjusted to adapt to different application scenarios and data set characteristics. The performance of the classifier can also be further improved through steps such as feature selection and data preprocessing.

[0071] This segment has important application value in the Paas cloud platform. The Paas cloud platform usually provides a large amount of computing resources and data storage capabilities, allowing users to easily deploy and run various machine learning models. By deploying this segment on the Paas cloud platform, real-time monitoring and classification of network traffic can be achieved, thereby timely identifying and blocking malicious traffic and improving network security and stability. In addition, the scalability and flexibility of the Paas cloud platform allow users to adjust and optimize the model according to actual needs, thereby continuously improving the performance and adaptability of the classifier.

[0072] Use machine learning technology to classify and identify normal and malicious traffic. By using two different classification algorithms, support vector machine and random forest, the performance and stability of the classifier can be improved, and the risk of misjudgment and missed judgment can be reduced. This method has broad application prospects in the Paas cloud platform and can provide effective technical support for network security and traffic management.

[0073] 3. Complex network management

[0074] Utilize graph algorithms and distributed computing methods to optimize the management of large-scale and complex network topologies and improve network resource utilization.

[0075] The Page Rank algorithm is a classic graph algorithm that can be used to measure the importance and influence of web pages. In the field of network security, the Page Rank algorithm can be used to sort and filter nodes in the network topology structure to discover important nodes and key paths in the network topology structure. Specifically, the network topology structure can be regarded as a directed graph, in which nodes represent network devices or hosts, and edges represent network connections or data flows. The Page Rank algorithm can be used to model and analyze the network topology structure to discover key nodes and weak links in the network topology structure.

[0076] Through the application of distributed computing and graph algorithms, the utilization and management efficiency of network resources can be effectively improved. Compared with the traditional centralized method, this method has significant advantages, especially in dealing with the challenges of large-scale and complex network topology management. The following is a detailed description of the implementation process.

[0077] First, we start by creating a Spark session and SparkContext. Spark session is the interface for interacting with the Spark cluster, which provides access to Spark SQL, DataFrame, and DatasetAPI. SparkContext is the main entry point for Spark applications, which is used to communicate with the Spark cluster and schedule computing tasks. In this process, creating Spark session and SparkContext is the basis of distributed computing, because they provide the necessary environment and resources for subsequent data processing and computing.

[0078] Next, the node and edge data of the network topology graph are defined. The node data represents each device or point in the network, while the edge data represents the connection relationship between these nodes. In some embodiments, the node and edge data are constructed into a GraphFrame object. GraphFrame is an API for processing graph data structures in Spark. It combines the functions of SparkSQL, DataFrame, and GraphX, allowing users to process graph data in a distributed and parallel manner.

[0079] Once the graph object is created, the Page Rank algorithm is used to calculate the importance of each node. The Page Rank algorithm was originally used by Google to measure the importance of web pages. It iteratively calculates the Page Rank value of each node to indicate the importance of the node in the entire graph. This algorithm is particularly suitable for network topology because it can identify key nodes in the network, thereby helping to optimize the allocation and management of network resources. In this section, the specific implementation of the Page Rank algorithm is completed through the pageRank method of GraphFrame. The parameters set the reset probability and tolerance to control the convergence and accuracy of the algorithm.

[0080] Then, the calculated Page Rank values ​​are extracted and the nodes are sorted by importance. The sorted node list shows the importance of each node, helping to identify which nodes play a key role in the network. This information is very useful for optimizing the network topology because it can guide network administrators to allocate more resources and take protective measures on key nodes and paths.

[0081] Finally, the sorted node list is output and the Spark session is closed. The output node list shows the Page Rank value of each node, arranged from high to low. This sorting helps network administrators quickly identify and focus on the most important nodes. Closing the Spark session is a standard operation to release resources and end computing tasks, ensuring that the resources of the distributed computing environment are properly managed.

[0082] In the context of Paas cloud platforms, this segment has important value and innovation. Paas cloud platforms usually provide powerful computing and storage resources, allowing users to easily deploy and run various large-scale data processing tasks. By utilizing distributed computing methods and graph algorithms, this segment can efficiently process large-scale and complex network topology data and improve network resource utilization and management efficiency. Specifically, compared with traditional centralized methods, this combination of distributed computing and graph algorithms can better cope with the challenges of large-scale network topology management and provide more accurate node sorting and critical path identification.

[0083] The improvement is to make full use of PySpark's parallel computing and distributed processing capabilities. PySpark is Spark's Python interface, which inherits all the advantages of Spark and provides a simple and easy-to-use programming interface, allowing Python developers to easily perform distributed data processing. By introducing the Page Rank algorithm, this section can effectively measure the importance of network nodes, so as to better sort and filter nodes. Compared with traditional methods, this improved solution not only has a faster processing speed, but also can more efficiently discover key nodes and weak links in the network topology, enhancing the stability and security of the system.

[0084] Distributed computing and graph algorithms are used to optimize large-scale and complex network topology management. Through parallel computing and distributed processing, large-scale network data can be efficiently processed, and network resource utilization and management efficiency can be improved. The introduction of the PageRank algorithm makes the importance assessment of nodes more accurate, thereby better optimizing the network and protecting key nodes. With the support of the Paas cloud platform, this method can provide powerful computing power and flexible resource management, providing an effective solution for network topology optimization and management.

[0085] 4. Page Rank algorithm improvements

[0086] The Page Rank algorithm is used to perform weight evaluation and resource allocation in network management to further improve network performance and stability.

[0087] In modern cloud computing environments, managing and optimizing cloud resources has become increasingly important and complex. Traditional security group technology has been unable to meet actual needs when faced with large-scale and complex network topologies. Therefore, optimizing and upgrading security group technology using technologies such as deep learning, graph algorithms, and distributed computing can effectively improve network security performance and respond to growing network security threats. This embodiment will describe in detail a solution for weight evaluation and resource allocation of virtual machines in a cloud platform based on an improved Page Rank algorithm, which improves the stability and security of the system by introducing weight adjustment, security, and reliability factors.

[0088] First, you need to understand the Page Rank algorithm and its application in cloud platform virtual machine management. The Page Rank algorithm was originally used by Google to rank web pages. It determines the ranking of each page in the search results by calculating the importance of each page. The core idea of ​​this algorithm is that if a page is linked by many other important pages, then this page is also considered important. In the cloud platform, virtual machines and network connections can be viewed as a directed graph, where each node represents a virtual machine or a network device, and each edge represents the connection relationship between virtual machines or between virtual machines and network devices. By modeling and analyzing this directed graph, the Page Rank algorithm can be used to sort and filter virtual machines by weight, thereby discovering important virtual machines and critical paths in the network topology.

[0089] In order to adapt to the characteristics of the cloud platform, the Page Rank algorithm needs to be improved to improve its stability and security. The improved Page Rank algorithm introduces weight adjustment, which sets weights for nodes according to the characteristics and performance indicators of the virtual machine, so that it can more accurately reflect the importance and resource requirements of the virtual machine. In addition, security and reliability factors are introduced, considering the security and reliability indicators of the virtual machine, and adding corresponding weight adjustments to the Page Rank calculation to improve the security and stability of the system. In the process of weight evaluation and resource allocation, it is also necessary to consider the constraints of virtual machine resources, such as memory, CPU, etc., to ensure the rationality and balance of resource allocation.

[0090] First, when constructing a directed graph of virtual machine nodes, each node represents a virtual machine or network device, and each edge represents the connection relationship between virtual machines or between virtual machines and network devices. Then, according to the characteristics and performance indicators of the virtual machine, an initial weight is set for each node. These indicators may include the computing power, memory capacity, storage space, etc. of the virtual machine. These initial weights will be used for subsequent Page Rank calculations.

[0091] In the calculation process of Page Rank algorithm, the damping factor is introduced. The damping factor is a constant, usually taken as 0.85, which is used to simulate the behavior of users randomly clicking on pages. Specifically, the Page Rank value of each node is equal to the sum of the evenly distributed Page Rank value and the damping factor multiplied by the Page Rank values ​​of other nodes connected to the node. The evenly distributed Page Rank value means that in the network, the initial Page Rank value of each node is evenly distributed to all nodes, while the damping factor multiplied by the sum of the Page Rank values ​​of other nodes connected to the node represents the importance of these nodes to the current node.

[0092] In order to improve the security and stability of the system, the security and reliability factors of the virtual machine also need to be considered. The security factor reflects the security of the virtual machine in the network, that is, the virtual machine is less likely to be attacked or fail. The reliability factor represents the reliability of the virtual machine, that is, the ability of the virtual machine to run stably and provide continuous services. In the Page Rank calculation, the Page Rank value of the node can be corrected by introducing the weight adjustment of these two factors. Specifically, the security score and reliability score of the node can be combined with the damping factor, so that nodes with higher security and reliability receive higher weights in the calculation, which are reflected in the final Page Rank value.

[0093] Calculating the safety score and reliability score of a node is an important step in improving the Page Rank algorithm. The safety score can be determined by calculating the number of safe paths between nodes. Assuming that there are several intermediate nodes on the path from node i to node k, the number of safe paths from node i to node k can be calculated by multiplying the number of safe paths and the weight of all intermediate nodes on the path. By traversing all node pairs and intermediate nodes, the safety score of each node can be obtained. The calculation method of the reliability score is similar to the safety score, which is determined by calculating the number of reliable paths between nodes. The number of reliable paths can be calculated by multiplying the number of reliable paths and the weight of all intermediate nodes on the path. Finally, the safety score and reliability score of each node can be obtained by adding the number of safe paths and the number of reliable paths of all node pairs respectively and dividing by the sum of the out-degrees of the nodes.

[0094] Once the security score and reliability score of each node are calculated, the improved Page Rank algorithm can be used to iteratively calculate the Page Rank value of the node. First, the average distributed Page Rank value of each node is calculated, and then for each node, the sum of the Page Rank values ​​of other nodes connected to the node is calculated, and the weight adjustment of security and reliability factors is considered. Through multiple iterations, until the Page Rank value of the node converges or the maximum number of iterations is reached, the final node Page Rank value can be obtained.

[0095] Finally, virtual machine nodes can be sorted and filtered according to the Page Rank value of the node. Nodes with higher Page Rank values ​​indicate that they are more important in the network. These nodes are often the nodes with the most concentrated traffic or the nodes with the greatest influence in the network, so they need special attention and protection. After identifying these key nodes, stricter security measures can be taken on these nodes, such as strengthening firewall rules, increasing traffic monitoring, etc., to improve the security of the entire network. In addition, nodes or paths with low Page Rank values ​​but connecting important nodes represent weak links in the network topology. If these nodes or paths are attacked or fail, they may have a greater impact on the entire network. Therefore, timely discovery and strengthening of these weak links can significantly improve the stability and security of the network.

[0096] In order to apply this solution to actual cloud platforms, further integration and optimization are needed. Cloud platforms usually provide powerful computing and storage resources, allowing users to easily deploy and run various large-scale data processing tasks. The above virtual machine weight evaluation and resource allocation code based on the improved Page Rank algorithm can be deployed on the cloud platform to use its distributed computing capabilities to process and analyze network data.

[0097] The specific implementation steps include the following aspects: Collect node and edge data in the network through network monitoring tools or log systems. Organize this data into an appropriate format for subsequent graph construction and analysis. Build a PySpark environment on the cloud platform and install necessary dependency libraries such as GraphFrames. Configure Spark session and SparkContext to support distributed computing. Use the collected node and edge data to build a GraphFrame graph object. Call the Page Rank method to calculate the Page Rank value of each node and set appropriate parameters to ensure the convergence and accuracy of the algorithm. Sort the nodes according to the Page Rank value and filter out key nodes. These nodes are the most important nodes in the network and require special attention and protection. Identify the weak links in the network topology by analyzing nodes or paths with low Page Rank values ​​but connecting important nodes. Strengthen these weak links to improve the stability and security of the network. Integrate the above steps into an automated process to regularly collect and analyze network data and update the importance and weak link information of nodes in real time. Improve the security and management efficiency of the entire network through continuous monitoring and optimization.

[0098] Through the above steps, an efficient virtual machine weight evaluation and resource allocation solution can be implemented on the cloud platform. Using distributed computing and graph algorithms, large-scale and complex network data can be efficiently processed, and network resource utilization and management efficiency can be improved. At the same time, through the improved Page Rank algorithm, the importance of virtual machines can be accurately measured and key nodes and weak links in the network can be discovered, so as to take targeted security measures and enhance the stability and security of the system.

[0099] In network management, using the Page Rank algorithm to evaluate node weights and allocate resources can improve network performance and stability. Specifically, the Page Rank algorithm is a classic graph algorithm that can be used to measure the importance and influence of nodes. In network management, the network topology can be viewed as a directed graph, where nodes represent network devices or hosts, and edges represent network connections or data flows. By modeling and analyzing the network topology, the Page Rank algorithm can be used to sort and filter nodes, thereby discovering important nodes and key paths in the network topology.

[0100] In the cloud platform, the Page Rank algorithm can be used to evaluate the weights of virtual machines and allocate resources, so as to better manage and optimize cloud resources. Specifically, virtual machines and network connections can be regarded as a directed graph, in which each node represents a virtual machine or a network connection, and each edge represents the connection relationship between virtual machines or between virtual machines and the network. By modeling and analyzing the directed graph, the Page Rank algorithm can be used to sort and screen the virtual machines by weight, so as to discover important virtual machines and critical paths in the network topology. The improved Page Rank algorithm proposed in the present invention mainly adopts the following improvement measures: Introduce weight adjustment: According to the characteristics and performance indicators of the virtual machine, set weights for the nodes so that they can more accurately reflect the importance and resource requirements of the virtual machine. Introduce security and reliability factors: Consider the security and reliability indicators of the virtual machine, and add corresponding weight adjustments in the Page Rank calculation to improve the security and stability of the system. Consider resource constraints: In the process of weight evaluation and resource allocation, consider the constraints of virtual machine resources, such as memory, CPU, etc., to ensure the rationality and balance of resource allocation. The specific mathematical formula is as follows:

[0101] Define symbols:

[0102] N: represents the total number of nodes in the directed graph.

[0103] M: represents the total number of edges in the directed graph.

[0104] i: indicates the index of the current virtual machine node.

[0105] PR(i1: represents the Page Rank value of node i.

[0106] PR(j): represents the Page Rank value of node j.

[0107] d: represents the damping factor, usually taken as 0.85.

[0108] The iterative formula of the Page Rank algorithm is as follows:

[0109] PR(i)=(1-d) / N+d*∑(PR(j) / L(j));

[0110] Among them, i represents the index of the current virtual machine node, j represents the index of the virtual machine node connected to the current node, and L(i) represents the out-degree of node j, that is, the number of edges connected to node j. This formula shows that the Page Rank value of node i is equal to the damping factor multiplied by the sum of the Page Rank values ​​of nodes j connected to node i, plus (1-d) / N. Among them, (1-d) / N represents the average distribution of Page Rank values, and d*∑(PR(j) / L(j)) represents the distribution through the Page Rank values ​​of other nodes connected to node i. Through iterative calculation, the Page Rank value of each node can be obtained.

[0111] This formula can be used to calculate the Page Rank value of each virtual machine node, thereby weighting and screening the virtual machines and optimizing resource allocation and management.

[0112] The Page Rank algorithm provides a global node evaluation and resource allocation method that comprehensively considers the connection relationship and importance between nodes, rather than being limited to local indicators such as degree centrality. This makes the optimization scheme more comprehensive and accurate, better adaptable to complex cloud computing virtualization environments, and improves system performance and reliability. In addition, by using iterative calculations and convergence mechanisms, the optimization scheme can efficiently handle large-scale virtual machine nodes and complex network topologies, and has good scalability and adaptability.

[0113] In the cloud platform, in order to improve the system stability and security, it is necessary to improve the strategy of the Page Rank algorithm to better adapt it to the characteristics of the cloud platform. Specifically, nodes can be divided into three categories: virtual machine nodes, physical machine nodes, and network device nodes. At the same time, security factors and reliability factors can be introduced to perform weighted calculations on the Page Rank values ​​of nodes.

[0114] Define symbols:

[0115] N: represents the total number of nodes in the directed graph.

[0116] M: represents the total number of edges in the directed graph.

[0117] i: represents the index of the current node.

[0118] PR(i): represents the Page Rank value of node i.

[0119] PR(j): represents the Page Rank value of node j.

[0120] d: represents the damping factor, usually taken as 0.85.

[0121] α: represents the weight of the safety factor, usually set to 0.1.

[0122] β: represents the weight of the reliability factor, usually set to 0.2.

[0123] P(i): represents the security score of node i.

[0124] Q(i): represents the reliability score of node i.

[0125] s(i, k): represents the number of safe paths from node i to node k.

[0126] r(i, k): represents the number of reliable paths from node i to node k.

[0127] The iterative formula of the improved Page Rank algorithm is as follows:

[0128] PR(i)=(1-d) / N+d*∑((PR(j) / L(j))*(1-α*P(i)-β*Q(i)));

[0129] The formula for calculating the safety score and reliability score of a node is as follows:

[0130] P(i)=∑(s(i,k)) / ∑(L(i));

[0131] Q(i)=∑(r(i,k)) / ∑(L(i));

[0132] Calculate the safety score of the node:

[0133] Assume that the path from node i to node k is i->n1->n2->…->nk, where ni represents the intermediate node on the path.

[0134] The number of safe paths from node i to node k can be calculated by the following formula:

[0135] s(i,k)=Π(s(ni-1,ni)*c1(ni));

[0136] Among them, s(ni-1, ni) represents the number of safe paths from node ni-1 to node ni, and c1(ni) represents the security weight from node ni to nk. Therefore, the number of safe paths from node i to node k can be calculated by the following formula:

[0137] s(i,k)=∑(Π(s(ni-1,ni)*c(ni)));

[0138] Among them, ni traverses all intermediate nodes from node i to node k. By calculating the number of safe paths of a node, the safety score of the node can be calculated:

[0139] P(i)=∑(s(i,k)) / ∑(L(i));

[0140] Among them, k traverses all nodes, and L(i) represents the out-degree of node i, that is, the number of edges connected to node i.

[0141] Calculate the reliability score of the node:

[0142] Assume that the path from node i to node k is i->n1->n2->…->nk, where ni represents the intermediate node on the path.

[0143] The number of reliable paths from node i to node k can be calculated by the following formula:

[0144] r(i,k)=Π(r(ni-1,ni)*c2(ni));

[0145] Among them, r(ni-1, ni) represents the number of reliable paths from node ni-1 to node ni, and c2(ni) represents the reliability weight from node ni to nk.

[0146] Substituting the above formula into the Page Rank algorithm iteration formula, the improved Page Rank algorithm iteration formula is as follows:

[0147] PR(i)=(1-d) / N+d*∑(PR(j) / L(j))*(1-α*P(i)-β*Q(i));

[0148] Where i represents the index of the current node, j represents the index of the node connected to the current node, and L(j) represents the out-degree of node j, that is, the number of edges connected to node j. This formula indicates that the Page Rank value of node i is equal to the damping factor multiplied by the sum of the Page Rank values ​​of nodes j connected to node i, plus (1-d) / N. Where (1-d) / N represents the average distribution of Page Rank values, and d*∑(PR(j) / L(j))*(1-α*P(i)-β*Q(i)) represents the distribution based on the Page Rank values ​​of other nodes connected to node i, and introduces security factors and reliability factors for weighted calculation.

[0149] To calculate the safety score and reliability score of a node, the following formula can be used:

[0150] P(i)=∑(s(i,k)) / ∑(L(i));

[0151] Q(i)=∑(r(i,k)) / ∑(L(i));

[0152] Among them, s(i, k) represents the number of safe paths from node i to node k, and r(i, k) represents the number of reliable paths from node i to node k. By calculating the security score and reliability score of the node, the Page Rank value of the node can be weighted to better manage and optimize cloud resources.

[0153] Compared with the traditional Page Rank algorithm, the improved Page Rank algorithm introduces weighted calculation of security factors and reliability factors in the calculation of node Page Rank values, thereby better ensuring the security and stability of the system. At the same time, by calculating the security score and reliability score of the node, cloud resources can be better managed and optimized, improving the performance and efficiency of the system.

[0154] Assume that there is a cloud computing virtualization platform, which contains 10 virtual machine nodes connected by a network. It is hoped that the improved Page Rank algorithm can be used to evaluate the weights of these nodes and allocate resources to improve the security and stability of the system.

[0155] First, we build a directed graph of virtual machine nodes, where the edges represent the connection between nodes. We also define the out-degree of a node, which is the number of edges connected to the node. Next, we start iterating and calculating the Page Rank value of the node.

[0156] Before calculating the Page Rank value of a node, it is necessary to calculate the security score and reliability score of each node. Assume that the weight α of the security factor is 0.1 and the weight β of the reliability factor is 0.2.

[0157] For the calculation of the safety score, all node pairs are traversed and the number of safe paths from node i to node k is calculated. The number of safe paths can be calculated by multiplying the number of safe paths of the intermediate nodes on the path between the nodes and the weight. By traversing all node pairs and intermediate nodes, the number of safe paths from node i to node k can be obtained. Finally, the number of safe paths for all node pairs is added up and divided by the sum of the out-degrees of the nodes to obtain the safety score P(i) of the node.

[0158] For the calculation of reliability score, a similar method as safety score is adopted. All node pairs are traversed and the number of reliable paths from node i to node k is calculated. The number of reliable paths can be calculated by multiplying the number of reliable paths of the intermediate nodes on the path between nodes and the weight. By traversing all node pairs and intermediate nodes, the number of reliable paths from node i to node k can be obtained. Finally, the number of reliable paths of all node pairs is added up and divided by the sum of the out-degrees of the nodes to obtain the reliability score Q(i) of the node.

[0159] Now, the Page Rank value of the node can be calculated using the iterative formula of the improved Page Rank algorithm. During the iteration process, the Page Rank value of node i is represented as PR(i). According to the iterative formula, the evenly distributed Page Rank value (1-d) / N is first calculated, where d is the damping factor and N is the total number of nodes. Then, for each node j connected to node i, the Page Rank value of node j is divided by its out-degree L(j), multiplied by the weighted value of the safety score and reliability score, multiplied by the damping factor d, and finally all the products are added together. Finally, the new Page Rank value of node i is obtained.

[0160] Through iterative calculation and convergence mechanism, until the Page Rank value of the node converges or reaches the maximum number of iterations, the final node Page Rank value can be obtained. According to the Page Rank value of the node, the virtual machine nodes can be sorted and screened to optimize resource allocation and management.

[0161] The improved Page Rank algorithm takes into account the security and reliability factors of the nodes. By introducing weighted values ​​of security scores and reliability scores, the security and stability of the system are better guaranteed. By iteratively calculating the PageRank value of the node, cloud resources can be optimized and managed to improve the performance and efficiency of the system. By weight evaluation and resource allocation of nodes, important nodes and critical paths can be better protected, the stability and security of the system can be improved, and excessive concentration of resources and unbalanced load can be avoided, reducing the risk and vulnerability of the system. Such improvements have important value and innovation in the Paas cloud platform, which can help optimize resource management, improve system performance and reliability, and take into account the needs of security and stability.

[0162] 5. Implement security groups

[0163] By combining the above algorithms and technologies, we can achieve gradual optimization and upgrading of security group technology, effectively respond to network security challenges, and improve overall security performance.

[0164] In the cloud computing environment, network security and traffic management are becoming more and more complex and challenging. Traditional security group technology has been unable to meet the growing security needs. Therefore, it is necessary to optimize and upgrade security group technology using technologies such as deep learning, graph algorithms, and distributed computing. This paper describes in detail a solution for optimizing and managing large-scale and complex network topologies based on the Page Rank algorithm. The solution aims to improve network resource utilization, enhance system stability and security, and implement this technology on a PaaS cloud platform.

[0165] First, you need to understand the Page Rank algorithm and its application in network topology management. The Page Rank algorithm was originally used by Google to rank web pages, and it determines the ranking of each page in the search results by calculating its importance. The core idea of ​​this algorithm is that if a page is linked by many other important pages, then this page is also considered important. In network topology management, network nodes and the connection relationship between them can be modeled as a directed graph, where nodes represent network devices or points, and edges represent the connections between them. Through the Page Rank algorithm, the importance of each node can be calculated, thereby identifying the key nodes in the network.

[0166] In order to solve the management problem of large-scale and complex network topology, it is first necessary to build a topology graph of the network. This process involves collecting node and edge data in the network and representing this data as a graph structure. Node data can include various devices in the network, such as servers, routers, switches, etc., while edge data represents the connection relationship between these devices. This data can usually be collected through network monitoring tools or log systems and organized into an appropriate format.

[0167] Next, we will use Python and the PySpark library to implement this solution. PySpark is a Python interface for Spark that provides powerful distributed computing capabilities and can process large-scale data. After creating a Spark session and SparkContext, we build the node and edge data into a GraphFrame object. GraphFrame is an API for processing graph data structures in Spark. It combines the functions of Spark SQL, DataFrame, and GraphX, allowing graph data to be processed in a distributed and parallel manner.

[0168] After building the graph object, use the Page Rank algorithm to calculate the importance of each node. Specifically, the Page Rank algorithm iteratively calculates the Page Rank value of each node, which represents the importance of the node in the entire network. In the code, call the pageRank method of GraphFrame and set appropriate parameters such as reset probability and tolerance to control the convergence and accuracy of the algorithm. The result of the Page Rank algorithm will be the Page Rank value of each node, which can be used to measure the importance of the node.

[0169] After calculating the importance of the nodes, these nodes are sorted and the key nodes are screened. The sorted node list shows the Page Rank value of each node, arranged from high to low, so that network administrators can quickly identify and focus on the most important nodes. These key nodes are often the nodes with the most concentrated traffic in the network or the nodes with the greatest influence, so they need special attention and protection. After identifying these key nodes, stricter security measures can be taken on these nodes, such as strengthening firewall rules, increasing traffic monitoring, etc., thereby improving the security of the entire network.

[0170] In addition, through the application of Page Rank algorithm, weak links in the network topology can also be discovered. Weak links usually refer to those nodes or paths with low Page Rank values ​​but connecting important nodes. If these nodes or paths are attacked or fail, they may have a greater impact on the entire network. Therefore, timely discovery and strengthening of these weak links can significantly improve the stability and security of the network.

[0171] In order to apply this solution to the actual Paas cloud platform, further integration and optimization are needed. In the Paas cloud platform, powerful computing and storage resources are usually provided, allowing users to easily deploy and run various large-scale data processing tasks. The above-mentioned network topology management code based on the Page Rank algorithm can be deployed on the Paas cloud platform to use its distributed computing capabilities to process and analyze network data.

[0172] like Figure 2 A method for optimizing network topology management based on security groups on Paas cloud platform is proposed. The specific implementation steps include the following aspects: Collect node and edge data in the network through network monitoring tools or log systems. Organize these data into appropriate formats for subsequent graph construction and analysis. Build a PySpark environment on the Paas cloud platform and install necessary dependency libraries such as GraphFrames. Configure Spark session and SparkContext to support distributed computing. Use the collected node and edge data to build a GraphFrame graph object. Call the pageRank method to calculate the PageRank value of each node and set appropriate parameters to ensure the convergence and accuracy of the algorithm. Sort the nodes according to the Page Rank value and filter out key nodes. These nodes are the most important nodes in the network and need special attention and protection. Identify the weak links in the network topology by analyzing nodes or paths with low Page Rank values ​​but connecting important nodes. Strengthen these weak links to improve the stability and security of the network. Integrate the above steps into an automated process to regularly collect and analyze network data and update the importance and weak link information of nodes in real time. Improve the security and management efficiency of the entire network through continuous monitoring and optimization.

[0173] Through the above steps, an efficient network topology management solution can be implemented on the Paas cloud platform. Using distributed computing and graph algorithms, large-scale and complex network data can be efficiently processed, and network resource utilization and management efficiency can be improved. At the same time, through the application of the Page Rank algorithm, the importance of nodes can be accurately measured and key nodes and weak links in the network can be discovered, so as to take targeted security measures and enhance the stability and security of the system.

[0174] The future application scenarios of the security group technology based on the PaaS cloud platform proposed in the present invention are very extensive. First, in the field of cloud computing, the technology can be widely used in the field of cloud security, such as cloud security monitoring, cloud security auditing, cloud security policy formulation, etc. In addition, the technology can also be applied to the information security field of various enterprises and organizations, such as network security management in the fields of finance, telecommunications, and government. At the same time, the technology can also provide efficient, intelligent, and secure network resource management and optimization services for various large organizations.

[0175] The present invention also has high application value. First, the technology can improve the security performance and stability of cloud computing services, and improve users' trust and satisfaction with cloud services. In addition, the technology can also provide differentiated security services for cloud computing services, thereby improving the market competitiveness of enterprises. At the same time, the technology can also provide enterprises and organizations with efficient network resource management and optimization services, thereby improving the operational efficiency and cost control capabilities of enterprises.

[0176] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A security group implementation method based on Paas cloud platform, characterized in that: include: Collect and process network traffic data to form a data set containing normal and malicious traffic; Based on the data set, the LSTM network is used to analyze and predict network security threats in real time; Combine SVM and random forest algorithms to detect normal and malicious traffic; Optimize network topology management using graph algorithms and distributed computing methods.

2. A security group implementation method based on Paas cloud platform as claimed in claim 1, characterized in that: In the data set, the proportion of normal traffic is 80%, and the proportion of malicious traffic is 20%; the data in the data set is preprocessed and feature extracted, including data cleaning, data normalization, data dimension reduction and feature selection.

3. A security group implementation method based on Paas cloud platform as claimed in claim 1, characterized in that: The LSTM network adopts a network structure including two LSTM layers and one fully connected layer, selects cross entropy as the loss function, adopts the Adam optimization algorithm for network training, and divides the data set into a training set and a test set in a ratio of 8:

2.

4. A security group implementation method based on Paas cloud platform as claimed in claim 1, characterized in that: The combination of SVM and random forest algorithm is used to detect and classify normal traffic and malicious traffic, specifically: SVM classifier and random forest classifier are used to detect normal traffic and malicious traffic at the same time; when the SVM classifier and random forest classifier are trained on the training data, the performance of each classifier is evaluated by calculating the accuracy, precision and recall rate for the detection results of the two classifiers, and the SVM classifier and random forest classifier are optimized respectively by adjusting the kernel function type of the SVM and the number and depth of the trees in the random forest.

5. A security group implementation method based on Paas cloud platform as claimed in claim 1, characterized in that: The use of graph algorithms and distributed computing methods to optimize network topology management specifically includes: Collect node and edge data in the network through network monitoring tools or log systems. The virtual machines and network connections are considered as a directed graph, where each node represents a virtual machine and each edge represents the connection relationship between virtual machines. Build a PySpark environment on the Paas cloud platform, install the dependent library GraphFrames, and configure Spark session and SparkContext; Use the collected node and edge data to construct a GraphFrame graph object; Call the Page Rank method to calculate the Page Rank value of each node in the GraphFrame graph object; Sort the nodes according to Page Rank values ​​and filter out key nodes; Allocate resources and set protection measures based on the selected key nodes.

6. A security group implementation method based on Paas cloud platform as claimed in claim 5, characterized in that: When constructing the GraphFrame graph object, an initial weight is set for each node according to the characteristics and performance indicators of the virtual machine.

7. A method for implementing a security group based on a Paas cloud platform as claimed in claim 5, characterized in that: In the process of calculating the Page Rank value of each node, the safety score and reliability score of the node are combined with the damping factor to correct the Page Rank value of the node.

8. A method for implementing a security group based on a Paas cloud platform as claimed in claim 7, characterized in that: The calculation formula of the Page Rank value of each node is as follows: PR(i)=(1-d) / N+d*∑((PR(j) / L(j))*(1-d*P(i)-β*Q(i))); Where PR(i) and PR(j) represent the Page Rank values ​​of node i and node j respectively; d represents the damping factor; N represents the total number of nodes in the directed graph; L(j) is the out-degree of node j, which represents the number of edges connected to node j; P(i) and Q(i) represent the safety score and reliability score of node i respectively; α represents the weight of the safety factor, and β represents the weight of the reliability factor.

9. A method for implementing a security group based on a Paas cloud platform as claimed in claim 8, characterized in that: The calculation process of the safety score and reliability score of the node i is as follows: Assume that the path from node i to node k is i->n1->n2->…->nk, where ni represents the intermediate node on the path; the number of safe paths s(i, k) and the number of reliable paths r(i, k) from node i to node k are calculated by the following formula: s(i,k)=Π(s(ni-1,ni)*c1(ni)); r(i,k)=Π(r(ni-1,ni)*c2(ni)); In the formula, s(ni-1, ni) represents the number of safe paths from node ni-1 to node ni, and c1(ni) represents the security weight from node ni to nk; r(ni-1, ni) represents the number of reliable paths from node ni-1 to node ni; c2(ni) represents the reliability weight from node ni to nk. The number of safe paths through the calculation node is used to calculate the safety score of the node: P(i)=∑(s(i,k)) / ∑(L(i)); Q(i)=∑(r(i,k)) / ∑(L(i)); Where L(i) is the out-degree of node i, which indicates the number of edges connected to node i.

10. A method for optimizing network topology management based on a Paas cloud platform security group, characterized in that: include: Collect node and edge data in the network through network monitoring tools or log systems. The virtual machines and network connections are considered as a directed graph, where each node represents a virtual machine and each edge represents the connection relationship between virtual machines. Build a PySpark environment on the Paas cloud platform, install the dependent library GraphFrames, and configure Spark session and SparkContext; Use the collected node and edge data to construct a GraphFrame graph object; Call the Page Rank method to calculate the Page Rank value of each node in the GraphFrame graph object; Sort the nodes according to Page Rank values ​​and filter out key nodes; Allocate resources and set protection measures based on the selected key nodes.

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